app.py

from flask import Flask, render_template, request, jsonify
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
import joblib



df= pd.read_csv("SPAM.csv")
df_data = df[["Message","Category"]]
# Features and Labels
df_x = df_data['Message']
df_y = df_data.Category
# Extract Feature With CountVectorizer
corpus = df_x.values.astype('U')
cv = CountVectorizer()
X = cv.fit_transform(corpus) # Fit the Data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, df_y, test_size=0.33, random_state=42)
#Naive Bayes Classifier
clf = MultinomialNB()
clf.fit(X_train,y_train)
clf.score(X_test,y_test)


app = Flask(__name__)
#app.config['SERVER_NAME']="flaskapp:80"
@app.route('/')
def index():
	return render_template('form.html')

@app.route('/predict', methods=['POST','GET'])
def predict():
    if request.method == 'POST':
        name = request.form['comment']
        data = [name]
        vect = cv.transform(data).toarray()
        my_prediction = clf.predict(vect)
        result = "Not a Spam"
        if my_prediction == 'spam':
            result="Spam"
            return jsonify({'error':result})
        return jsonify({'name':result})
    elif request.method=='GET':
        name = request.args.get('commentInput')
        data = [name]
        vect = cv.transform(data).toarray()
        my_prediction = clf.predict(vect)
        result = "Not a Spam"
        if my_prediction == 'spam':
            result="Spam"
            return jsonify({"input":name,'outcome':result})
        return jsonify({'input':name,'outcome':result})

	
	# email = request.form['email']
	# name = request.form['name']

	# if name and email:
	# 	newName = name[::-1]

	# 	return jsonify({'name' : newName})

	# return jsonify({'error' : 'Missing data!'})

if __name__ == '__main__':
	app.run(host="192.168.29.254",port="80",debug=True)